IBM Launches Accelerated Discovery Lab

PORTLAND, Ore. — Today, IBM unveiled its Accelerated Discovery Lab at ADLab in San Jose, Calif., which aims to augment the data mining concept in practical application domains with smart analytics derived from its Watson question-and-answer (Q&A) technology combined with deep domain knowledge for each area being researched.

Jeff Welser, director of strategy and program development at IBM, runs its new Accelerated Design Lab.
(Source: IBM)

Jeff Welser, director of strategy and program development at IBM, in an interview with EETimes, said:

The Accelerated Discovery Lab shifts the focus away from just looking through big-data question-and-answer style. Instead of looking for answers that are already known -- where its just a matter of finding them -- we are learning how do search for things that are not yet known.

According to Welser, the biggest problem with big-data is not just sifting through it for answers, but also how to use it to intelligently infer new principles of relevance to specific domain problems. To remedy, IBM has already assembled domain expertise in biology, medicine, finance, weather, mathematics, computer science, and information technology, and is also currently extending into materials science. The goal is to accelerate the pace of discovery in each of these areas by automating the process of uncovering new governing principles in each domain. Welser said:

The significance of Moore's Law for big-data is not so much that the amount of data is doubling every year, but rather in how one can discover which elements of that data are relevant, which can actually be utilized, and which will provide more context when trying to solve specific problems.

Of the many algorithms used at the Accelerated Discovery Lab, some were derived from the Q&A algorithms used by IBM's Watson who beat the human experts in Jeopardy, and IBM is also working to instill cognitive abilities into their algorithms that allow the discovery of patterns hitherto unperceived by human experts. Using a combination of rule-based knowledge, statistical machine learning, and pattern recognition, the ADLab is seeking to mimic brainstorming combined with human-like intuition. In materials science, for instance, The Adlab is seeking to understand how different materials work so that its algorithms can make recommendations as to which new combinations might be worth considering in the future. Welser said:

We are getting closer and closer to having the machine make its own hypotheses, permitting it to make recommendations about what materials combinations to try next. But to do so you need to include more domain-specific knowledge. For instance, for electronic materials discovery it needs to understand what an insulator is, what a conductor is -- that is the level of intelligence we are going to put into these tools next.

The ADLab is aiming its expertise at a broad swath of industries, including retail, medicine, and finance, but currently its most developed areas of expertise are drug discovery, social media analytics, and predictive maintenance, and next on its list is materials science. Welser continued:

We are in the process of expanding into the discovery space of materials science. For instance, what if I need to find a new insulator, or a new conducting material, or even a transistor? We want to discover what new combinations of materials might be interesting to try, and whether there are ideas out there for new types of materials that have not been tried yet.

The use of algorithms is not an attempt to replace researchers, but rather to augment their abilities by weeding out the irrelevant connections and offering potential new connections that researchers might have found themselves if only they had been able to sift through the millions of relevant papers and patents. Welser told us:

Superconducting materials are a good example, since they are a complicated combination of alloys, so there are lots of different elements that could be tried. What we can do in the Accelerated Discovery Lab is look through literature about materials used for different purposes and figure out whether that material has a property that might make it worth looking at for superconducting applications.

It seems that IBM is trying to develop something like domain specific ADIs (Synonyms of API for Domain specific tools). It is also rightly depicted in the article that this is not going to replace the researchers but it will be helping a lot to the researcher and will be opening many new directions for the researchers.

We often gain insights as we daydream or shower; apparently the brain has available bandwidth to solve an unexpected problem. I wonder if the same phenomenon will eventuially occur in computers. As the data mining computer is tabulating the weekly payroll, will it suddenly blurt out "corn yields in fields could be doubled if you ..." or "car efficiency could be doubled if ..."

IBM's biggest success at the AD Lab, so far, is Big Pharm, for which it now has deep domain knowledge programmed into their algorithms for new drug discovery. Now it wants to do the same thing for materials science with relevance to new materials for electronics. They told me a story about how one researchers of theirs was looking for a new low-k dilectric, and happened to be speaking to a group of scientists developing new polymers who had a formulation that fit the bill. Now what they want to do is program in a deep domain knowledge of materials, so that their algorithms could find that polymer from searches of through the papers presented by those scientists, rather than depend on serendipity.